ABSTRACT Lung transplantation is the therapy of choice for numerous patients suffering from end stage pulmonary failure. Unfortunately, long term outcomes after lung transplantation remain disappointing with 5-year graft survival rates of only 50%, which lag substantially behind other solid organ transplants. The two most common events that can occur early after lung transplantation and that predispose to late graft failure are ischemia reperfusion injury-mediated primary graft dysfunction (PGD) and acute rejection (AR). PGD occurs in up to 25% of lung transplant recipients and leads to enhanced mortality. AR occurs in more than half of patients and leads to increased risk of chronic rejection. While current dogma clearly suggests that innate and adaptive immune responses play significant roles in the etiologies of PGD and AR, there has been almost no effort to define a potential role of viruses in PGD, and there have been few unbiased, systematic efforts to evaluate viral associations with AR. For lung transplant recipients, persistent or latent viruses within the lung itself, as well as viruses systemically circulating in the blood that could infect the lung de novo, could potentially impact outcomes in lung transplantation. The advent of Next Generation Sequencing (NGS) has enabled for the first time, systematic and unbiased analysis of the virome (i.e. the complete spectrum of viruses) of a given biological specimen. Critically, efforts to define the virome, and its potential association with disease in the context of lung transplantation are sorely lacking. The goals of the R61 Phase are to prospectively collect specimens from a cohort of lung transplant recipients and define statistical associations of the virome with PGD and or AR. The goals of the R33 phase are to validate results from the R61 in an independent cohort, characterize the viruses that are associated with PGD and/or AR by establishing cell culture and mouse models, and finally to define the impact of viral infection in a mouse lung transplant model on PGD and/or AR.